Fitness Landscape Based Parameter Estimation for Robust Taboo Search

  • Andreas Beham
  • Erik Pitzer
  • Michael Affenzeller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)


Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the algorithms’ performances with respect to different problem- and problem instance characteristics.


Problem Instance Problem Size Fitness Landscape Quadratic Assignment Problem Large Problem Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Numerical Insights. CRC Press (2009)Google Scholar
  2. 2.
    Bischl, B., Mersmann, O., Trautmann, H., Preuss, M.: Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012), pp. 313–320 (2012)Google Scholar
  3. 3.
    Chicano, F., Luque, G., Alba, E.: Autocorrelation measures for the quadratic assignment problem. Applied Mathematics Letters 25, 698–705 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Glover, F.: Tabu search – part I. ORSA Journal on Computing 1(3), 190–206 (1989)CrossRefzbMATHGoogle Scholar
  5. 5.
    Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica, Journal of the Econometric Society 25(1), 53–76 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Pitzer, E., Affenzeller, M.: A Comprehensive Survey on Fitness Landscape Analysis. In: Fodor, J., Klempous, R., Araujo, C.P.S. (eds.) Recent Advances in Intelligent Engineering Systems. SCI, vol. 378, pp. 161–191. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Pitzer, E., Beham, A., Affenzeller, M.: Generic hardness estimation using fitness and parameter landscapes applied to robust taboo search and the quadratic assignment problem. In: Companion Publication of the 2012 Genetic and Evolutionary Computation Conference, pp. 393–400 (2012)Google Scholar
  8. 8.
    Taillard, E.D.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17, 443–455 (1991)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas Beham
    • 1
  • Erik Pitzer
    • 1
  • Michael Affenzeller
    • 1
  1. 1.School of Informatics, Communications and MediaUniversity of Applied Sciences Upper AustriaHagenbergAustria

Personalised recommendations